TY - GEN
T1 - Predicting Depression and Anxiety
T2 - 8th International Conference on Medical and Health Informatics, ICMHI 2024
AU - Fong, David
AU - Chu, Tianshu
AU - Heflin, Matthew
AU - Gu, Xiaosi
AU - Seneviratne, Oshani
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/5/17
Y1 - 2024/5/17
N2 - We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions.
AB - We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions.
KW - Data Analysis
KW - Health Informatics
KW - Machine Learning
KW - Mental Health Trends
UR - https://www.scopus.com/pages/publications/85204602486
U2 - 10.1145/3673971.3673995
DO - 10.1145/3673971.3673995
M3 - Conference contribution
AN - SCOPUS:85204602486
T3 - ACM International Conference Proceeding Series
SP - 325
EP - 331
BT - ICMHI 2024 - 2024 8th International Conference on Medical and Health Informatics
PB - Association for Computing Machinery
Y2 - 17 May 2024 through 19 May 2024
ER -